Digitalisation of battery testing, from cell to system level, including lifetime assessment (Batteries Partnership)

Topic description

ExpectedOutcome:

Project results are expected to contribute to all of the following expected outcomes:

• Competitiveness of the European battery industry across the value chain (from cell manufacturers to cell integrators);
• Shorter time-to-market;
• Reduced time and/or cost of battery development by at least 20% to 30%;
• Improved battery design, for longer lifetime, and better reliability and safety;
• Reduced investment and operational costs of battery systems.

The current way of developing batteries is mainly based on trial-and-error processes, which are time consuming, costly, and do not always lead to the best product design. It is particularly the case when it comes to testing batteries to assess their performance, lifetime, reliability and safety. Existing methods and tools lead to high costs, because of long test durations, and/or the high number of required test samples, and/or the use of costly test infrastructures. There is a significant room for improvement, by relying on digital methods and tools to minimise the use of standard trial-and-error processes. Digitalisation of battery testing will lead to an acceleration of the battery development time, a higher quality of the battery assessment (better evaluation of battery performances, lifetime, reliability and safety), and an improvement of the battery design itself (by better adapting the design to the application requirements and production capabilities) and a better estimation lifetime (by better modelling of battery ageing). Improvement in battery testing will result in major cost savings, in particular in the development phase (test before invest).

Projects are expected to provide novel methods and tools to accelerate and improve the battery testing process. A multi-scale approach should be used, by covering the value chain from battery cells to battery systems (here, a battery system refers to an energy storage unit integrating battery cells, excluding power converters). Projects should propose and validate a new paradigm based on intelligent design of experiment (to avoid duplicated experiments, or experiments that give low-quality information), the smart combination of physical and virtual testing, hardware in the loop solutions, and the development and use of advanced models describing battery cells and systems (physics-based models, data-driven models, or hybrid models) and the relevant expected evolution in multiple different conditions of usage. In turn, this requires full documentation of new modules, models or tools developed from scratch or substantially improved. Particular attention should be paid to the assessment of battery lifetime, reliability and safety, including the development of innovative methods for testing of safety in transport and safety in usage, based on representativeness of the method for the various potential failures (failure initiation, propagation control, mitigation means, etc…).Projects should have an ambition for cross-sectorial applications, and should focus on battery chemistries currently on the market or that will reach the market in the short term (i.e., advanced lithium-ion chemistries), with the potential to quickly adapt to next-generation battery chemistries (i.e., solid-state lithium-based chemistries).

This topic implements the co-programmed European Partnership on ‘Towards a competitive European industrial battery value chain for stationary applications and e-mobility’.

Activities are expected to achieve TRL 5-6 by the end of the project – see General Annex B.

Digital Agenda
Co-programmed European Partnerships
Artificial Intelligence